Can computers learn to understand language nuances like humans? This paper presents an unsupervised algorithm for discovering inference rules from text, addressing the mismatch between questions and text expressions. The algorithm expands on Harris’ Distributional Hypothesis, applying it to paths within dependency trees of parsed text. It proposes that paths linking similar words likely share similar meanings. This approach allows the system to uncover inference rules that might be overlooked by human analysts. The discovered rules could help in addressing one of the main challenges in question-answering.
This paper on discovering inference rules for question answering aligns with Natural Language Engineering's focus on the computational aspects of language. By presenting an algorithm for automated rule discovery, the research contributes to the journal's scope of advancing NLP techniques for processing and understanding natural language.
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|---|---|
| Technology: Engineering (General). Civil engineering (General) | 1 |
| Science: Mathematics | 1 |